Tufts University - Graduate School of Arts and Sciences
MSc in Data Analytics
Medford, USA
MSc
DURATION
5 years
LANGUAGES
English
PACE
Full time, Part time
APPLICATION DEADLINE
EARLIEST START DATE
STUDY FORMAT
On-Campus
Key Summary
About: The MSc in Data Analytics at Tufts Graduate School of Arts and Sciences blends a liberal arts atmosphere with research resources. An advisory board of industry professionals guides rigorous coursework in analytical methods used across the arts, humanities, sciences and business. Tufts offers mentoring, labs and industry partnerships. IBM projects demand for data professionals will rise by 364,000 to 2,720,000 by 2020. Keywords: data science, statistics, big data.
Career Outcomes: Graduates enter roles such as data analyst, data scientist, business intelligence analyst, machine learning engineer, data engineer and statistician, working across tech, finance, healthcare, government, academia and non-profit sectors.
Data analytics is a fast-growing field and often the key to business strategy and the solution to complex questions. Data analysts are sought after in nearly every field. In fact, in a recent study, IBM predicts that by 2020 demand for data professionals will increase by 364,000 job openings to 2,720,000.
The interdisciplinary Master of Science in Data Analytics is guided by an advisory board of professionals in the field, who help ensure the program provides rigorous courses in the analytical skills that are sought after across the arts, humanities, and sciences as well as within the business community.
The program is offered on both a full-time or part-time schedule for working professionals who are looking to build expertise in the field of data analytics. Most students complete the program in two years, but it can be completed in one year with accelerated study.
Structure
This interdisciplinary master’s program is designed to serve students in a broad range of fields. As a student in the program, you’ll tailor your course selection based on your individual interests and professional goals.
The structure of the curriculum includes five components:
- Foundations of Data Analytics: 1 course
This required introductory course will provide you with a strong foundation on the general topic of data analytics - Application & Professional Competencies: 5 courses (4 of these are ½ semester courses)
These required courses allow you to develop key competencies demanded by employers in a rapidly growing field. You’ll learn how to communicate and present data effectively through courses ranging from data visualization to coding. - Statistical Analysis and Modeling Techniques: 3 courses
This section is a deep dive into statistical analysis and techniques in analytic methods. - Disciplinary Competencies: 2 courses
Choose from a series of discipline-related electives that will allow you to narrow your focus and apply the skills you’ve learned to your area of interest. - Capstone: 1 course
You’ll culminate the program by completing an experimental capstone project that will incorporate and demonstrate the skills and knowledge you’ve gained.
Upon completion of the program, you will be able to demonstrate the following areas of knowledge:
- Define and solve complex data-based problems using appropriate statistical methodologies analyses.
- Select appropriate statistical and predictive methodologies with both sparse and large data sets.
- Provide appropriate theoretical interpretation of these results based on discipline-related concepts.
- Demonstrate written oral communication skills for conclusions drawn from the analyses of data.
- Create visual representations to increase understanding and utilization of complex data.
- Maintain collaborative team relationships to effectively contribute to a shared project.
- Have functional programming skills in the data-related language.
